SoroushVahidi/scidocs huggingface dataset:
WiMo: location-based emotion tagging
(2009)

In this paper we introduce WiMo, a location-based social networking tool that enables users to share and store their emotional feelings about places. WiMo creates a mobile social network based on common interests and enables users to share not just information but also opinions, experiences and passions. The application uses a geo-emotional tagging system running on a GPS enabled mobile phone to ascribe emotions to places. In this paper we describe the development of an emotion tagging interface and present a case study scenario to situate the application in a real world setting. We then describe work-in-progress on the development of the WiMo prototype interface, and the interaction process for users. We further outline the features of the system and finally discuss next steps in the development of the application.
Addressing DODAG inconsistency attacks in RPL networks
(2014)

RPL is a routing protocol for low-power and lossy constrained node networks. A malicious node can manipulate header options used by RPL to track DODAG inconsistencies, thereby causing denial of service attacks, increased control message overhead, and black-holes at the targeted node. RPL counteracts DODAG inconsistencies by using a fixed threshold, upon reaching which all subsequent packets with erroneous header options are ignored. However, the fixed threshold is arbitrary and does not resolve the black-hole issue either. To address this we present a mitigation strategy that allows nodes to dynamically adapt against a DODAG inconsistency attack. We also present the forced black-hole attack problem and a solution that can be used to mitigate it. Results from our experiments show that our proposed approach mitigates these attacks without any significant overhead.
Episodic and semantic memory in mild cognitive impairment
(2005)

Little is known about episodic and semantic memory in the early predementia stage of Alzheimer's disease (AD), which is referred to as mild cognitive impairment (MCI). To explore person knowledge, item recognition and spatial associative memory, we designed the Face Place Test (FPT). A total of 75 subjects participated: 22 patients with early AD, 24 with MCI and 29 matched controls. As predicted, AD patients showed significant deficits in person naming, item recognition and recall of spatial location (placing). Surprisingly, subjects with MCI were also impaired on all components. There was no significant difference between AD and MCI except on the placing component. Analysis of the relationship between semantic (naming) and episodic (recognition and placing) components of the FPT revealed a significant association between the two episodic tasks, but not between episodic and semantic performance. Patients with MCI show deficits of episodic and semantic memory. The extent of impairment suggests dysfunction beyond the medial temporal lobe. The FPT might form the basis of a sensitive early indicator of AD.
Cryptanalysis of DES Implemented on Computers with Cache
(2003)

This paper presents the results of applying an attack against the Data Encryption Standard (DES) implemented in some applications, using side-channel information based on CPU delay as proposed in [11]. This cryptanalysis technique uses side-channel information on encryption processing to select and collect effective plaintexts for cryptanalysis, and infers the information on the expanded key from the collected plaintexts. On applying this attack, we found that the cipher can be broken with 2 known plaintexts and 2 calculations at a success rate > 90%, using a personal computer with 600-MHz Pentium III. We discuss the feasibility of cache attack on ciphers that need many S-box look-ups, through reviewing the results of our experimental attacks on the block ciphers excluding DES, such as AES.
On the importance of accurate weak classifier learning for boosted weak classifiers
(2008)

Recent work has shown that improving model learning for weak classifiers can yield significant gains in the overall accuracy of a boosted classifier. However, most published classifier boosting research relies only on rudimentary learning techniques for weak classifiers. So while it is known that improving the model learning can greatly improve the accuracy of the resulting strong classifier, it remains to be shown how much can yet be gained by further improving the model learning at the weak classifier level. This paper derives a very accurate model learning method for weak classifiers based on the popular Haar-like features and presents an investigation of its usefulness compared to the standard and recent approaches. The accuracy of the new method is shown by demonstrating the new models ability to predict ROC performance on validation data. A discussion of the problems in learning accurate weak hypotheses is given, along with example solutions. It is also shown that a previous simpler method can be further improved. Lastly, we show that improving model accuracy does not continue to yield improved overall classification beyond a certain point. At this point the learning technique, in this case RealBoost, is unable to make gains from the improved model data. The method has been tested on pedestrian detection tasks using classifiers boosted using the RealBoost boosting algorithm. A subset of our most interesting results is shown to demonstrate the value of method.
Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network
(2017)

In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.
A Spiking Neural Network Based Cortex-Like Mechanism and Application to Facial Expression Recognition
(2012)

In this paper, we present a quantitative, highly structured cortex-simulated model, which can be simply described as feedforward, hierarchical simulation of ventral stream of visual cortex using biologically plausible, computationally convenient spiking neural network system. The motivation comes directly from recent pioneering works on detailed functional decomposition analysis of the feedforward pathway of the ventral stream of visual cortex and developments on artificial spiking neural networks (SNNs). By combining the logical structure of the cortical hierarchy and computing power of the spiking neuron model, a practical framework has been presented. As a proof of principle, we demonstrate our system on several facial expression recognition tasks. The proposed cortical-like feedforward hierarchy framework has the merit of capability of dealing with complicated pattern recognition problems, suggesting that, by combining the cognitive models with modern neurocomputational approaches, the neurosystematic approach to the study of cortex-like mechanism has the potential to extend our knowledge of brain mechanisms underlying the cognitive analysis and to advance theoretical models of how we recognize face or, more specifically, perceive other people's facial expression in a rich, dynamic, and complex environment, providing a new starting point for improved models of visual cortex-like mechanism.
LINE: Large-scale Information Network Embedding
(2015)

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the ``LINE,'' which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online\footnote{\url{https://github.com/tangjianpku/LINE}}.
Reverse wire bonding and phosphor printing for LED wafer level packaging
(2012)

Solid state lighting is a good alternative light source with reduced energy consumption. Light-emitting diode (LED) is very efficient in turning electrical energy into light. LED has a number of advantages over the traditional light sources. The optical performance of the LED component is very critical. In general, white light can be obtained by applying phosphor on a blue LED chip. The blue light from the LED excites the phosphor to emit yellow light. The blue and yellow light mixes together to give white light. In order to obtain a good optical performance, it is necessary to apply phosphor properly. It is challenging to distribute a right amount of phosphor on the LED die. Besides, phosphor dispensing is usually the slowest process when compared with die bonding and wire bonding. This controls the overall throughput of the LED packaging process. There are different methods to apply the phosphor. The phosphor is mixed with epoxy or silicone to form slurry and is then dispensed onto the chip. However, the spatial color distribution is poor if phosphor slurry is used. Conformal phosphor coating can be used to improve the spatial color distribution. In this paper, an innovative phosphor stencil printing method is proposed. This paper demonstrates the feasibility of the phosphor stencil printing process for wafer-level LED packaging. LEDs are first mounted on a wafer submount. Wire bonds are used as interconnect. The phosphor is stencil printed on the chip surface after wire bond. The minimum phosphor layer thickness is controlled by the wire bond loop height. In order to achieve a low loop height, reverse wire bonding is used. The first bond is on the wafer submount and the second bond is on the LED chip. The reverse wire bond has a very low profile which allows a thin layer of phosphor to be printed on the chip surface. Prototypes are successfully fabricated. A uniform layer of phosphor is stencil printed on the LED chip on the wafer submount. Experimental result shows that the proposed phosphor printing method is very effective in distributing the right amount of phosphor on the chip surface.
5G cellular network integration with SDN: Challenges, issues and beyond
(2017)

The tremendous growth in communication technology is shaping a hyper-connected network where billions or connected devices are producing a huge volume of data. Cellular and mobile network is a major contributor towards this technology shift and require new architectural paradigm to provide low latency, high performance in a resource constrained environment. 5G technology deployment with fully IP-based connectivity is anticipated by 2020. However, there is no standard established for 5G technology and many efforts are being made to establish a unified 5G stander. In this context, variant technology such as Software Defined Network (SDN) and Network Function virtualization (NFV) are the best candidate. SDN dissociate control plane from data plane and network management is done on the centralized control plane. In this paper, a survey on state of the art on the 5G integration with the SDN is presented. A comprehensive review is presented for the different integrated architectures of 5G wireless network and the generalized solutions over the period 2010–2016. This comparative analysis of the existing solutions of SDN-based cellular network (5G) implementations provides an easy and concise view of the emerging trends by 2020.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
(2018)

Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements (p < 0.01) over the state-of-the-art in both a human observer study and the semantic interpretability score.
A Pose-Invariant Descriptor for Human Detection and Segmentation
(2008)

We present a learning-based, sliding window-style approach for the problem of detecting humans in still images. Instead of traditional concatenation-style image location-based feature encoding, a global descriptor more invariant to pose variation is introduced. Specifically, we propose a principled approach to learning and classifying human/non-human image patterns by simultaneously segmenting human shapes and poses, and extracting articulation-insensitive features. The shapes and poses are segmented by an efficient, probabilistic hierarchical part-template matching algorithm, and the features are collected in the context of poses by tracing around the estimated shape boundaries. Histograms of oriented gradients are used as a source of low-level features from which our pose-invariant descriptors are computed, and kernel SVMs are adopted as the test classifiers. We evaluate our detection and segmentation approach on two public pedestrian datasets.
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style
(2018)

How much does “free shipping!” help an advertisement’s ability to persuade? This paper presents two methods for performance attribution: finding the degree to which an outcome can be attributed to parts of a text while controlling for potential confounders1. Both algorithms are based on interpreting the behaviors and parameters of trained neural networks. One method uses a CNN to encode the text, an adversarial objective function to control for confounders, and projects its weights onto its activations to interpret the importance of each phrase towards each output class. The other method leverages residualization to control for confounds and performs interpretation by aggregating over learned word vectors. We demonstrate these algorithms’ efficacy on 118,000 internet search advertisements and outcomes, finding language indicative of high and low click through rate (CTR) regardless of who the ad is by or what it is for. Our results suggest the proposed algorithms are high performance and data efficient, able to glean actionable insights from fewer than 10,000 data points. We find that quick, easy, and authoritative language is associated with success, while lackluster embellishment is related to failure. These findings agree with the advertising industry’s emperical wisdom, automatically revealing insights which previously required manual A/B testing to discover.
A survey on comparison of electric motor types and drives used for electric vehicles
(2014)

In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.
Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image
(2017)

We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.
Think-aloud protocols: Analyzing three different think-aloud protocols with counts of verbalized frustrations in a usability study of an information-rich Web site
(2010)

We describe an empirical, between-subjects study on the use of think-aloud protocols in usability testing of an information-rich Web site. This double-blind study used three different types of think-aloud protocols: a traditional protocol, a speech-communication protocol, and a coaching protocol. A silent condition served as the control. Eighty participants were recruited and randomly pre-assigned to one of four conditions. With the goal of keeping unintended bias to a minimum, data analysis did not count the number of identified usability problems by condition, which was considered too subjective. Rather, the study collected the number of verbalized and non-verbalized counts of frustration by condition that users experienced. The study also did a count of the number of verbalized and non-verbalized instances of positive comments by condition that users expressed. Results show that there were no statistical differences in the number of counts by condition with respect to the traditional, speech communication, or coaching condition. The study concludes that simply counting the verbalizations of users by condition does not give enough information to determine whether any of the conditions would lead to a better understanding of the usability problems associated with the Web site.
An empirical study on the use of i* by non-technical stakeholders: the case of strategic dependency diagrams
(2018)

Early phases of information systems engineering include the understanding of the enterprise’s context and the construction of models at different levels of decomposition, required to design the system architecture. These time-consuming activities are usually conducted by relatively large teams, composed of groups of non-technical stakeholders playing mostly an informative role (i.e. not involved in documentation and even less in modelling), led by few experienced technical consultants performing most of the documenting and modelling effort. This paper evaluates the ability of non-technical stakeholders to create strategic dependency diagrams written with the i* language in the design of the context model of a system architecture, and find out which difficulties they may encounter and what the quality of the models they build is. A case study involving non-technical stakeholders from 11 organizational areas in an Ecuadorian university held under the supervision and coordination of the two authors acting as consultants. The non-technical stakeholders identified the majority of the dependencies that should appear in the case study’s context model, although they experienced some difficulties in declaring the type of dependency, representing such dependencies graphically and applying the description guidelines provided in the training. Managers were observed to make more mistakes than other more operational roles. From the observations of these results, a set of methodological advices were compiled for their use in future, similar endeavours. It is concluded that non-technical stakeholders can take an active role in the construction of the context model. This conclusion is relevant for both researchers and practitioners involved in technology transfer actions with use of i*.
A high-PSR LDO using a feedforward supply-noise cancellation technique
(2011)

A feed-forward noise cancellation (FFNC) technique to improve the power supply noise rejection (PSR) of a low dropout regulator (LDO) is presented. The proposed FFNC operates in conjunction with a conventional LDO and extends the noise rejection bandwidth by nearly an order of magnitude. Fabricated in 0.18µm CMOS, at 10mA load current, the prototype achieves a PSR of −50dB and −25dB at 1MHz and 10MHz supply noise frequencies, respectively. Compared to a conventional LDO, this represents an improvement of at least 30dB at 1MHz and 15dB at 10MHz. The prototype uses only 20pF load capacitance and occupies an active area of 0.04mm2.